Abstract: This research study presents the recognition of fingers grasps for various grasping styles of daily living. In general, the posture of the human hand determines the fingers that are used to create contact between an object at the same time while developing the touching contact. Human grasping can detect by studying the movement of fingers while bending during object holding. Ten right-handed subjects are participated in the experiment; each subject was fitted with a right-handed GloveMAP, which recorded all movement of the thumb, index, and middle of human fingers while grasping selected objects. GloveMAP is constructed using flexible bend sensors placed back of a glove. Based on the grasp human taxonomy by Cutkosky, the object grasping is distinguished by two dominant prehensile postures; that is, the power grip and the precision grip. The dataset signal is extracted using GloveMAP, and all the signals are filtered using Gaussian filtering method. The method is capable to improving the amplitude transmission characteristic with the minimal combination of time and amplitude response. The result was no overshoot in order to smoothen the grasping signal from unneeded signal (noise) that occurs on the input / original grasping data. Principal Component Analysis – Best Matching Unit (PCA-BMU) is a process of justifying the human grasping data involves several grasping groups and forming a component identified as nodes or neuron.